The growing penetration of intermittent, renewable generation in US power
grids, especially wind and solar generation, results in increased operational
uncertainty. In that context, accurate forecasts are critical, especially for
wind generation, which exhibits large variability and is historically harder to
predict. To overcome this challenge, this work proposes a novel
Bundle-Predict-Reconcile (BPR) framework that integrates asset bundling,
machine learning, and forecast reconciliation techniques. The BPR framework
first learns an intermediate hierarchy level (the bundles), then predicts wind
power at the asset, bundle, and fleet level, and finally reconciles all
forecasts to ensure consistency. This approach effectively introduces an
auxiliary learning task (predicting the bundle-level time series) to help the
main learning tasks. The paper also introduces new asset-bundling criteria that
capture the spatio-temporal dynamics of wind power time series. Extensive
numerical experiments are conducted on an industry-size dataset of 283 wind
farms in the MISO footprint. The experiments consider short-term and day-ahead
forecasts, and evaluates a large variety of forecasting models that include
weather predictions as covariates. The results demonstrate the benefits of BPR,
which consistently and significantly improves forecast accuracy over baselines,
especially at the fleet level.

通过资产捆绑、机器学习和预测协调技术，本文提出了一种称为 Bundle-Predict-Reconcile（BPR）的新型框架，针对美国电网中不断增加的间歇性、可再生发电，尤其是风力发电，提供了准确的预测，加强了其在预测一致性方面的表现。